Week 5 Snaps Report

Week 5 Snaps Report

The Week 5 Snaps Report gives fantasy players a view into the Team’s system, positional usages and player activities. Does the team use RBs more than WRs? Does the team rely on their WRs? These are key questions for lineups, DFS plays, and waiver wire selections. These metrics strengthen as the season goes on. Please come back and continue following my work!


Landscape Data Informatics for my

Week 5 Snaps Report

I believe one way to fight the various biases we as Fantasy Players have to deal with is to use landscape metrics. This prevents the more common “Silo Effect” most “experts” deal out.

Not only is fantasy a weekly game it is a complex system. System-level predictions are tough.  However, innovation often comes from combining data from several sources. I interpret this as a call for fantasy players to use multi data approaches for this game. See the link for starting your exploring. 

make-better-decisions-combine-datasets

Consider the landscape views of multi-data veins that invite mining for informatic gold. This is my journey within Fantasy in a nutshell! I wish to “show” others my approaches as well.


Team Snaps Weeks 1 to 5, Average Snaps, and DIFFs 

As we have 5 weeks of data for the teams, I wanted to up the game here by combining multi-data sources and use ratio metrics for hypothesis formation. I begin by the landscape view. More Snaps associate with team speeds.  Fast Teams are more passing centric vs slower snaps teams are rushing heavy. (Data from my textbook studies).

Week 5 Snaps Report. This Table Below Includes:

  • Team (Colorized Speed of Team- Fast to Slow)
  • Week 1 to 5 Team Level Snaps High to Low (Colorization)
  • Avg of Snaps
  • DIFF (Snaps difference between the last 2 weeks of team play)

Note: I colorized the Team Names by Snap Average Blue to Red!

** In summary, I favor pass-catchers from faster teams and rushing RBs from slower teams

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A Plot of Snaps vs Points Scored with Trends

What teams are speeding up (passing) or slowing down (rushing). I watch for trend shifts to trade, drop, or acquire players. 

The plot reveals teams that increased snaps vs declined in snaps vs Snap Averages -Speed Left to Right (Fast to Slow Teams)

HOU/DAL/DET/NE/BAL/WAS/ OAK/MIN increased in SNAPs

LAR/CLE/BUF/CHI/KC/TEN/OAK/NYJ/MIA deceased in SNAPs

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Team Positional Level % of Team Snaps

I always suggest players use a broad view of a Team’s activities. I continue in this Snap Report by looking at the positions. Note, all metrics are colorized High to Low.

These tables from the Week 5 Snaps Report include:

  • Team
  • Position
  • Week 1 to 5 Snaps
  • Average of Player Snaps Per Week
  • % SNAP Based Positional Usage 
  • Bar Graph of the Positional Usages

These tables focus on the position level of each team. I suggest positional usages vis snap data is a data stream of importance. Looking for teams that highly use certain positions vs under-using others.

This data stream gets lost in the weekly matchups. Use these landscape metrics first to frame the weekly match-ups. Use the plots to slow scan this data to get the team backgrounds in positional usages.


 

ARI_ATL_BAL_BUF 

I find the extremes and use that data to move toward or away from players. I will let users scan the data and decide what key facts are germane to your teams. Find your own connections. Note the team’s success in their distributions. Are the snaps because of poor play etc or deliberate to winning? Deeper questioning! 

For Example

  • Several winning teams,  KC is TE/WR Biased and NE is also TE/WR Biased
  • Losing teams, MIA WR only, CIN also WR only etc. 
  • Nice research points for 2020! 

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CAR_CHI_CIN_CLE

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DAL_DEN_DET_GB

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HOU_IND_JAX_KC

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LAC_LAR_MIA_MIN

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NE_NO_NYG_NYJ

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OAK_PHI_PIT_SEA

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SF_TB_TEN_WAS

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The following tables present the position, weeks 1 to 5 snaps, Snaps Averages, % Usages as measured by TS% (% of Team Total Snaps) in all the Teams. The top positional using teams are stained in Green vs the low use teams in Red.

Bar Graph Plots allow a visual view of the league landscapes. Find the extremes.


 

A Plot of Running Back Team Snap Use

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A Plot of Tight End Team Snap Use

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A Plot of Wide Receiver Team Snap Use

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Running Back Player Snaps Week 1 to 5, Sum Snaps, and Diffs in Snaps. 

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Running Back Extremes DIFFs Snaps Between the Last Weeks of Team Activity. 

Sorted by Increased Snaps to Declines in Snaps. Why was there an extreme? Solve that for profit! 

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Tight Ends Player Snaps Week 1 to 5, Sum Snaps, and Diffs in Snaps. 

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Tight Ends Extremes DIFFs Snaps Between the Last Weeks of Team Activity. 

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Wide Receivers Player Snaps Week 1 to 5, Sum Snaps, and Diffs in Snaps.

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Wide Receivers Extremes DIFFs Snaps Between the Last Weeks of Team Activity. 

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Player Snaps By Teams (Player Environment Analysis)

 

Player Environment Analysis within Teams allows a deeper focus onto the player’s SNAP metrics. This data analysis can not be used correctly by 5 minutes of scanning. If you do not have the time for the task please use the player SNAPs data above. Week 5 Snaps Report.

I use the deeper Team Player Snaps environment analysis for lineups in seasonal and DFS, drops adds, handcuffs identification, previous week game scripts for positions usages and seeing the upcoming late-season bloomers.

I really suggest you finalize your teams by week 10 to 12 going into the playoffs. Use this data to help formula your trades and acquisitions. The DIFF metrics can spot new trends for your use. do not miss that player you need.


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Please use SNAPs data with my Rankings this week.

https://www.fakepigskin.com/2019/10/09/fantasy-football-week-6-rankings-with-uncertainty/

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